Bathtub-Shaped Failure Rate of Sensors for Distributed Detection and Fusion

Junhai Luo,Tao Li

doi:10.1155/2014/202950

Abstract

We study distributed detection and fusion in sensor networks with bathtub-shaped failure (BSF) rate of the sensors which may or not send data to the Fusion Center (FC). The reliability of semiconductor devices is usually represented by the failure rate curve (called the “bathtub curve”), which can be divided into the three following regions: initial failure period, random failure period, and wear-out failure period. Considering the possibility of the failed sensors which still work but in a bad situation, it is unreasonable to trust the data from these sensors. Based on the above situation, we bring in new characteristics to failed sensors. Each sensor quantizes its local observation into one bit of information which is sent to the FC for overall fusion because of power, communication, and bandwidth constraints. Under this sensor failure model, the Extension Log-likelihood Ratio Test (ELRT) rule is derived. Finally, the ROC curve for this model is presented. The simulation results show that the ELRT rule improves the robust performance of the system, compared with the traditional fusion rule without considering sensor failures.

Highlights

Distributed detection and decision fusion using multiple sensors have attracted significant attention because of their wide applications, such as security, traffic, battlefield surveillance, and environmental monitoring
Considering a wireless sensor network consisting of M distribution sensors and a Fusion Center (FC), each sensor makes a binary hypothesis decision; their decisions are sent to the FC to make a global decision which decides whether the target is present [1,2,3,4,5,6,7,8]
We have derived the Extension Log-likelihood Ratio Test (ELRT) rule based on bathtub-shaped failure (BSF) model for distributed target detection in sensor networks

Summary

Introduction

Distributed detection and decision fusion using multiple sensors have attracted significant attention because of their wide applications, such as security, traffic, battlefield surveillance, and environmental monitoring. In [9], the authors have proposed a new adaptive decentralized soft decision combining rule for multiple-sensor distributed detection systems with data fusion which does not require the knowledge of the false alarm and detection probabilities of the distributed sensors. In [18], the authors have studied a different approach and have constructed a fusion rule which is sensor-failure-robust by including a sensor failure model and minimizing expected Bayesian risk. We raise a new approach which expands the traditional log-likelihood ratio test by including a sensor failure model.

Sensor Mode

Failure of Sensors

Fusion Rule

Conclusion